class_docstr: Anomalib dataset.
  
  Args:
      task (str): Task type, either 'classification' or 'segmentation'
      transform (A.Compose): Albumentations Compose object describing the transforms that are applied to the inputs.
docstr: Concatenate this dataset with another dataset.
docstr_tok: ["Concatenate","this","dataset","with","another","dataset","."]
code: def __add__(self, other_dataset: AnomalibDataset) -> AnomalibDataset:
      """Concatenate this dataset with another dataset."""
      assert isinstance(
          other_dataset, self.__class__
      ), "Cannot concatenate datasets that are not of the same type."
      assert (
          self.is_setup
      ), "Cannot concatenate uninitialized datasets. Call setup first."
      assert (
          other_dataset.is_setup
      ), "Cannot concatenate uninitialized datasets. Call setup first."
      dataset = copy.deepcopy(self)
      dataset.samples = pd.concat(
          [self.samples, other_dataset.samples], ignore_index=True
      )
      return dataset
  
imports: from __future__ import annotations
  import copy
  import logging
  from abc import ABC, abstractmethod
  from pathlib import Path
  from typing import Sequence
  import albumentations
  import cv2
  import numpy
  import pandas
  from pandas import DataFrame
  from torch import Tensor
  from torch.utils.data import Dataset
  from anomalib.data.task_type import TaskType
  from anomalib.data.utils import masks_to_boxes, read_image
label_desc: Use image node_id 10 for calling the AnomalibDataset obj's underlying member method code with example usage: obj.__add__(other_dataset) and returns: dataset